一句话：提出自适应配置调优框架 AgileCtrl，以缓解现有方法自身仍需显式参数或隐式训练配置的问题。
软件系统越来越多地向用户暴露性能敏感配置参数（PerfConfs），但其合适取值难以确定且常随运行环境变化。已有的自适应框架可自动监控软件行为并动态调优配置，以在动态变化下维持期望性能，然而这些框架通常自身也需要配置：有时表现为额外的显式参数，有时则隐含在训练过程中。为此，论文提出 AgileCtrl，一个面向配置调优的自适应框架。给定摘要未展开其具体机制与实验结果，但核心目标是降低或消除自适应调优框架对自身配置或训练的依赖。
Software systems increasingly expose performance-sensitive configuration parameters, or PerfConfs, to users. Unfortunately, the right settings of these PerfConfs are difficult to decide and often change at run time. To address this problem, prior research has proposed self-adaptive frameworks that automatically monitor the software’s behavior and dynamically tune configurations to provide the desired performance despite dynamic changes. However, these frameworks often require configuration themselves; sometimes explicitly in the form of additional parameters, sometimes implicitly in the form of training.
DOI 原文 ·
@inproceedings{paperbot7026,
title = {AgileCtrl: a self-adaptive framework for configuration tuning},
author = {Shu Wang and Henry Hoffmann and Shan Lu},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3549136}
}